Proteome-wide computational analyses reveal links between protein condensate formation and RNA biology
Biomolecular condensates mediate dynamic compartmentalization of cellular processes. The multivalent interactions that underlie biomolecular condensation are often promoted by intrinsically disordered regions (IDRs) within proteins. While the role of IDRs in biomolecular condensates is well appreciated, predicting whether an IDR forms condensates in cells remains challenging. Here, we developed a machine learning model to predict condensation behavior of IDRs, analyzing 215 IDRs from fusion oncoproteins in HEK293T cells. Our study identified distinct sequence-derived physicochemical features associated with condensation. Leveraging these data, our model predicts that [~]12% of the [~]13,000 IDRs in the human proteome are likely to form cellular condensates. Proteins with condensate-forming IDRs are enriched in functions involving RNA-related processes and membrane-less organelles (MLOs), highlighting their role in MLO assembly and function. Our model, available via the SAK3.0 web server (https://sak.stjude.org), provides a powerful resource for studying IDR-driven phase separation across proteomes, offering insights into biomolecular condensates and their biological roles.